Updated
over 5 years
ago.
2 hacker ♥️.
Mozilla's DeepSpeech project[1] is using TensorFlow and some paper from Baidu to make an open source speech to text system, based on deep learning (TensorFlow). The current project allow the training for own local datasets, but also there is a pre-trained model that can be used during the development.
The goal of the project is:
- Connect to mumble or to the local audio stream
- Connect to etherpad
- Map the sound to text, and write it into the etherpad
- Have fun how funny accents break the system
- Redo the etherpad based on what you remember from the meeting and send it to the RESULT mailing list
[1] https://github.com/mozilla/DeepSpeech
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Make more sense of openQA test results using AI by livdywan
Description
AI has the potential to help with something many of us spend a lot of time doing which is making sense of openQA logs when a job fails.
User Story
Allison Average has a puzzled look on their face while staring at log files that seem to make little sense. Is this a known issue, something completely new or maybe related to infrastructure changes?
Goals
- Leverage a chat interface to help Allison
- Create a model from scratch based on data from openQA
- Proof of concept for automated analysis of openQA test results
Bonus
- Use AI to suggest solutions to merge conflicts
- This would need a merge conflict editor that can suggest solving the conflict
- Use image recognition for needles
Resources
Timeline
Day 1
- Conversing with open-webui to teach me how to create a model based on openQA test results
- Asking for example code using TensorFlow in Python
- Discussing log files to explore what to analyze
- Drafting a new project called Testimony (based on Implementing a containerized Python action) - the project name was also suggested by the assistant
Day 2
- Using NotebookLLM (Gemini) to produce conversational versions of blog posts
- Researching the possibility of creating a project logo with AI
- Asking open-webui, persons with prior experience and conducting a web search for advice
Highlights
- I briefly tested compared models to see if they would make me more productive. Between llama, gemma and mistral there was no amazing difference in the results for my case.
- Convincing the chat interface to produce code specific to my use case required very explicit instructions.
- Asking for advice on how to use open-webui itself better was frustratingly unfruitful both in trivial and more advanced regards.
- Documentation on source materials used by LLM's and tools for this purpose seems virtually non-existent - specifically if a logo can be generated based on particular licenses
Outcomes
- Chat interface-supported development is providing good starting points and open-webui being open source is more flexible than Gemini. Although currently some fancy features such as grounding and generated podcasts are missing.
- Allison still has to be very experienced with openQA to use a chat interface for test review. Publicly available system prompts would make that easier, though.